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Record W4210649231 · doi:10.1109/cdc45484.2021.9683654

Mean Field Stochastic Growth with Relative Utility and Common Noise

2021· article· en· W4210649231 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2021 60th IEEE Conference on Decision and Control (CDC) · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicStochastic processes and financial applications
Canadian institutionsCarleton University
Fundersnot available
KeywordsMean field theoryNonlinear systemStochastic processStochastic differential equationMathematicsHamilton–Jacobi–Bellman equationApplied mathematicsComputer scienceMathematical optimizationBellman equationStatistics

Abstract

fetched live from OpenAlex

We consider mean field games in the setting of stochastic growth where each player’s capital stock is described by Cobb-Douglas production dynamics subject to stochastic depreciation and common noise. We combine both one’s own utility and a relative utility to define the individual utility functions. Due to random mean field dynamics, the analysis of the best response relies on a stochastic Hamilton-Jacobi-Bellman equation, which in turn induces a special nonlinear backward SDE. We analyze this BSDE and use it to determine the solution equation system of the mean field game. We further extend the analysis to an AK model for growth dynamics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.241
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it